Artificial Intelligence and Game Theory: Insights into Decision-Making Algorithms bridges the gap between theoretical strategy and its real-world impact on AI systems. Organized in a fun, reference-style format, each section offers a stand-alone exploration of advanced topics like Generative Adversarial Networks, Bayesian belief formation, dynamic learning in games, evolutionary game theory, and cooperative game dynamics, as well as core fundamentals such as Nash equilibrium and minimax strategies.

Readers will find in-depth yet accessible discussions of essential ideas, complemented by a practical coding component in Jupyter Notebooks. This hands-on approach lets you experiment directly with Python-based models, guaranteeing that theoretical insights translate into concrete, problem-solving techniques.

Ideal for AI practitioners seeking to incorporate game theory into their work, this book is also an invaluable resource for students of computer science, economics, and philosophy. Game theorists aiming to expand into artificial intelligence will likewise find it indispensable. By blending an approachable style with rigorous content, Artificial Intelligence and Game Theory helps readers elevate their AI systems through strategic game-theoretic insights.

What you will learn:

• Key Game Theory Concepts: Delve into Nash Equilibrium, evolutionary game theory, adversarial dynamics, cooperative strategies, and more.
• Practical AI Applications: Apply game-theoretic models to real-world scenarios—autonomous vehicles, optimization tasks, multi-agent systems, and beyond.
• Hands-On Coding: Experiment with Jupyter Notebooks for Python-based implementations of game-theoretic strategies.
• Strategic AI Development: Elevate algorithmic decision-making in dynamic, competitive environments by integrating game theory into AI workflows. 

Who This Book Is For

Data scientists, and machine learning/deep learning engineers who seek to integrate game theory into their work to enhance decision-making processes. It also targets AI practitioners and developers looking to optimize decision-making algorithms through game theory principles.

Dr. Ashton T. Sperry-Taylor holds a B.A. in Mathematics and Philosophy (with honors) from Franklin & Marshall College and a Ph.D. in Philosophy from the University of Missouri–Columbia. His expertise encompasses artificial intelligence, decision and game theory, and the philosophy of science. In his commercial endeavors, he develops algorithms to analyze and forecast in-home behavioral patterns, detect anomalies, identify falls, and employ computer vision to assess mobility. His algorithms offer long-term, in-home support for caregivers of the elderly and individuals with cognitive disabilities. His research centers on using reinforcement learning to model dynamic belief revision in game theory, examining the explanatory power of equilibrium models in the social sciences, and exploring how agent-based computational models tackle the complexity of social behavior. His work appears in peer-reviewed journals and is presented at conferences throughout North America and internationally.